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Found 17 papers, 10 papers with code
POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning
In order to tackle these challenges simultaneously, in this paper, we introduce PrOmpt-based domaiN Discrimination (POND), the first framework to utilize prompts for time series domain adaptation.
Non-Euclidean Spatial Graph Neural Network
Besides, existing spatial network representation learning methods can only consider networks embedded in Euclidean space, and can not well exploit the rich geometric information carried by irregular and non-uniform non-Euclidean space.
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
In this article, we present a comprehensive survey on domain specification techniques for large language models, an emerging direction critical for large language model applications.
Deep Graph Representation Learning and Optimization for Influence Maximization
Influence maximization (IM) is formulated as selecting a set of initial users from a social network to maximize the expected number of influenced users.
DeepGAR: Deep Graph Learning for Analogical Reasoning
As the most well-known computational method of analogical reasoning, Structure-Mapping Theory (SMT) abstracts both target and base subjects into relational graphs and forms the cognitive process of analogical reasoning by finding a corresponding subgraph (i. e., correspondence) in the target graph that is aligned with the base graph.
Source Localization of Graph Diffusion via Variational Autoencoders for Graph Inverse Problems
Different from most traditional source localization methods, this paper focuses on a probabilistic manner to account for the uncertainty of different candidate sources.
An Invertible Graph Diffusion Neural Network for Source Localization
This paper aims to establish a generic framework of invertible graph diffusion models for source localization on graphs, namely Invertible Validity-aware Graph Diffusion (IVGD), to handle major challenges including 1) Difficulty to leverage knowledge in graph diffusion models for modeling their inverse processes in an end-to-end fashion, 2) Difficulty to ensure the validity of the inferred sources, and 3) Efficiency and scalability in source inference.
Edge Graph Neural Networks for Massive MIMO Detection
Massive Multiple-Input Multiple-Out (MIMO) detection is an important problem in modern wireless communication systems.
Do Multi-Lingual Pre-trained Language Models Reveal Consistent Token Attributions in Different Languages?
During the past several years, a surge of multi-lingual Pre-trained Language Models (PLMs) has been proposed to achieve state-of-the-art performance in many cross-lingual downstream tasks.
A Convergent ADMM Framework for Efficient Neural Network Training
As a well-known optimization framework, the Alternating Direction Method of Multipliers (ADMM) has achieved tremendous success in many classification and regression applications.
Towards Quantized Model Parallelism for Graph-Augmented MLPs Based on Gradient-Free ADMM Framework
Theoretical convergence to a (quantized) stationary point of the pdADMM-G algorithm and the pdADMM-G-Q algorithm is provided with a sublinear convergence rate $o(1/k)$, where $k$ is the number of iterations.
Sign-regularized Multi-task Learning
Multi-task learning is a framework that enforces different learning tasks to share their knowledge to improve their generalization performance.
pdADMM: parallel deep learning Alternating Direction Method of Multipliers
In this paper, we propose a novel parallel deep learning ADMM framework (pdADMM) to achieve layer parallelism: parameters in each layer of neural networks can be updated independently in parallel.
Tunable Subnetwork Splitting for Model-parallelism of Neural Network Training
In this paper, we analyze the reason and propose to achieve a compelling trade-off between parallelism and accuracy by a reformulation called Tunable Subnetwork Splitting Method (TSSM), which can tune the decomposition granularity of deep neural networks.
Gradient-free Neural Network Training by Multi-convex Alternating Optimization
To overcome these drawbacks, alternating minimization-based methods for deep neural network optimization have attracted fast-increasing attention recently.
ADMM for Efficient Deep Learning with Global Convergence
However, as an emerging domain, several challenges remain, including 1) The lack of global convergence guarantees, 2) Slow convergence towards solutions, and 3) Cubic time complexity with regard to feature dimensions.
Nonconvex Generalization of Alternating Direction Method of Multipliers for Nonlinear Equality Constrained Problems
The classic Alternating Direction Method of Multipliers (ADMM) is a popular framework to solve linear-equality constrained problems.
Optimization and Control Social and Information Networks
